As the Federal Communications Commission (FCC) moves towards a PSAP-level location accuracy mandate, improving methods for different location technologies becomes a necessity. The subject matter described herein relates to the fields of communications and location technology. It provides a means for improving the accuracy of location technologies such as Global Positioning System (GPS), Uplink Time Difference of Arrival (UTDOA) and Advanced Forward Link Trilateration (AFLT).
A common approach to position estimation is to find a weighted least squares solution from measured quantities such as time differences, pseudoranges or power levels. The weighted least squares solution is known to achieve a maximum likelihood (ML) solution when input errors are independent and Gaussian (see J. Caffery, Wireless Location in CDMA Cellular Radio Systems, Boston-London: Kluwer Academic Publishers, 2000), but it cannot do this under the more general conditions encountered in practice. For example, TDOA errors have a tendency to be positive relative to the predicted leading edge of the multipath delay profile. As explained below, several factors such as imperfect leading edge detection and non-line-of-sight (NLOS) propagation contribute to these positive errors. As a result, the per-baseline error distribution is skewed. This skew reduces the accuracy of the basic weighted least squares method. In contrast, the method described herein exploits knowledge of this skew to obtain improved results. Moreover, correlation among these errors can often be found; for example, distinct multipath components can be received at the same sector, common NLOS conditions may exist at a site and common errors may be introduced by the reference signal. These correlations may be incorporated into a maximum a posteriori (MAP) algorithm as described below. This framework can also be used to incorporate an estimate of the a priori mobile position distribution in the location solution.
UTDOA is a network-based technology allowing for any signal transmitted from any type of mobile station (MS) to be received at any base station to obtain a UTDOA measurement. A reference base station measures the received signal at roughly the same time as each cooperating base station, as illustrated in FIG. 1.
FIG. 1 shows an idealized model of the signals available to or from a mobile device for positioning where the present invention could be used to increase the accuracy of the positioning estimate (also called a location attempt). This figure also identifies the system components for wireless location. In FIG. 1, a Global Navigation Satellite System 101 (GNSS) such as the United States NavStar Global Positioning System (GPS), broadcasts well defined, code division multiple access (CDMA) spread spectrum signals 107 used by specially equipped mobile wireless devices 102 for TDOA location estimation of latitude, longitude, altitude and velocity. If the mobile device 102 is not equipped to receive satellite signals 107 for location calculation, both uplink and downlink terrestrial wireless techniques using TDOA or Time-of-Arrival (TOA) calculations may be used to provide a location estimate. Terrestrial wide area wireless location techniques using downlink (network based transmitter-to-device) TDOA or TOA techniques include Advanced Forward Link Trilateration (FLT) [IS-95, IS-2000], Enhanced Time Difference of Arrival (E-OTD) [GSM] and Observed Time Difference of Arrival (OTDOA) [UMTS] as well as distributed beacon techniques. Terrestrial Downlink techniques require that the mobile device 102 measure the downlink radio signals 108 from network based transmitters 103 104 and then use the radio link(s) 109, backhaul facilities 113 and the Wireless Communications Network 110 to convey the collected radio measurements to a Position Determining Entity (PDE) 106 for conversion into a latitude, longitude and in some cases an altitude.
Terrestrial wide area wireless location techniques using uplink (device-to-network based receiver) TDOA or TOA techniques include U-TDOA, U-TDOA/Angle of Arrival (AoA) hybrid and U-TDOA/Assisted GPS. U-TDOA and hybrids currently function in CDMA [IS-95, IS-2000], GSM, UMTS, WiMAX (802.16e/m and 802.20) and conceptually for the upcoming Long-Term-Evolution (LTE) OFDM based wireless radio access network (RAN). Terrestrial Uplink techniques require that the mobile device 102 transmissions 109 be measured by network based receivers (in this case co-located within the cell sites 103 104. Measurement data is then conveyed by backhaul 111 to a Position Determining Entity (PDE) 106 for conversion into a latitude, longitude, velocity, and in some cases an altitude. Regardless of the aforementioned wireless location technique, determination of the radio signal time-of-flight is key to accurate determination of the mobile devices 102 actual location. In FIG. 1, the real world influences of signal reflection, diffraction, and attenuation due to constructive or destructive interference are not shown.
In the system of FIG. 1, by cross-correlating the received signal at the reference base station with the received signal at a cooperating base station, a time difference of arrival is determined. The cooperating stations send their TDOA measurements to a position determining entity (PDE) where a location solution is found. However, impairments to the measurement can arise from additive noise and signal level fluctuations. These impairments may affect the sensitivity of detecting the presence of the mobile signal at the cooperating base station. Other impairments to the estimation impact the cooperator's ability to detect the line of sight (LOS) path delay.
FIGS. 2A, 2B, 2C, and 2D illustrate how objects, such as a building, may block the direct path, creating a non-line of sight impairment in different location environments, including uplink, downlink, GNSS and hybrid GNSS/uplink systems (where GNSS stands for Global Navigation Satellite System). A diffracted path traveling around a building arrives at the receiver later than the highly attenuated or completely blocked direct path. Additionally, reflections from obstacles can cause scattering, which produces dispersion of the arrival times of different paths. In FIG. 2A, an example of an uplink wireless location system is depicted. The mobile device 102 transmits a signal 109. In some cases, such as for the Reference Receiver 203, the radio signal is received directly (a line-of-sight or LOS case). But other receivers 104 may receive diffracted signal 202 or a reflected signal 203. In each case the original uplink signal 109 may also be received or have the original signal blocked, attenuated or delayed by an obstruction 201.
FIG. 3 illustrates impairments that make detection of the first arrival difficult and cause a skewing of the TDOA error. The reference numerals in FIG. 3 are used as follows:                303=Transmit time        304=Detection threshold        305=Line-of-sight (LOS) time-of-flight        306=Lag time        307=Basis for reported TOA or TDOA        308=Delay spread        309=Missed signal components        
FIG. 3 shows the arrival times of a multi-path degraded signal on an amplitude 302 to time 301 plot 300. A signal is transmitted at time 303 and has a potential direct path time-of-flight shown as 305. The earliest signal component arrivals are undetected since they arrive at a power level below the detection threshold 304. The detection threshold 304 must be maintained to avoid excessive false alarms. Missed earliest arrival detection events cause a reported TOA or TDOA that is larger than the LOS TOA or TDOA which is desired. In this example the first signal above threshold 307 produces a lag of 306 from the true first arriving signal component. Additionally, the earliest arriving multipath components may arrive later than expected due to NLOS propagation creating an NLOS delay. This also causes a reported TDOA that is larger than the LOS TDOA. These factors skew errors between the TDOA measurements and an LOS TDOA being searched or computed by positioning algorithms. Positioning decisions in the inventive solution described herein exploit both the skewing of errors caused by these factors as well as the non-Gaussian shape of the error distribution.
The method described in U.S. Pat. No. 6,564,065, May 13, 2003, K. Chang et al., “Bayesian-update based location prediction method for CDMA Systems,” appears to predict power levels from CDMA pilot channel measurements with location decisions made from an a posteriori power distribution using simulation. The method described in U.S. Pat. No. 5,252,982, Oct. 12, 1993, E. Frei, “Method of precise position determination,” appears to assume Gaussian errors using a weighted least squares method that iteratively finds phase ambiguities for a GPS location solution using an a posteriori RMS error.